Disentanglement.
Feature disentanglement is a wide and hot research topic.
We only focus on works that are closely related to ours.
In the image domain, some works consider adaptation from a generative view. [1]} learn a disentangled semantic representation across domains. A similar idea is then applied to graph domain adaptation [2]} and domain generalization [3]}. [4]} propose a novel informative feature disentanglement, equipped with the adversarial network or the metric discrepancy model. Another disentanglement-related topic is sequential data generation.
To generate videos, existing works [5]}, [6]}, [7]} extend VAE to a recurrent form with different recursive structures.
In this paper, we present a VAE-based structure to generate cross-domain videos. We aim at tackling the challenging video-based UDA from a new perspective: sequential domain disentanglement and transfer.
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